Modern organizations often need to access data to manage their enterprises effectively. Generally, more accurate and more timely data enables improved product quality, improved customer service, lower costs and higher profitability. The vast amount of capital resources, human resources and physical resources that a typical organization commits to collecting, processing and analyzing data typically indicates the value to an organization of accurate and timely data. Information regarding customer activity and preferences is of particular value. Customers are the key to success for most organizations, whether the customers are current revenue producing customers, potential customers or constituents in a government or non-profit organization. Therefore, organizations spend large amounts of time, effort and money collecting, monitoring, evaluating, analyzing and forecasting customer activity. The data collected about customers may be as broad as an industry or market study, or as narrow as how a particular demographic or individual customer responds to a directed solicitation. Organizations use this data, for example, to optimize operations, improve financial performance, formulate product strategy, target marketing efforts and formulate plans from the broadest strategic vision down to the most detailed operational detail.
One type of customer data that is often valuable to an organization relates to timing with which a customer or potential customer responds to a solicitation. For instance, rapid turnaround of a marketing solicitation may indicate a healthy, eager demand for a particular product or service. Similarly, quick payment of a bill may indicate a happy customer, a financially sound customer and/or a customer that prefers to minimize outstanding debt.
Detailed information related to customer payment habits is of particular interest to an organization's financial operation because the information is often used to better forecast cash flows, to modify billing procedures and to increase rapid payment of bills. Due to the importance of cash in running a business, it is usually in a company's best interest to collect outstanding receivables as quickly as possible. Organizations typically calculate the average collection period as the approximate amount of time that it takes for a business to receive payments owed from its customers and clients. Many businesses allow customers to purchase goods or services via credit, but one of the problems with extending credit is not knowing when the customer will make cash payments. Therefore, decreasing the average collection period is often desirable because this means that it does not take a company very long to turn its receivables into cash. See, for instance, http://www.investopedia.com for general information regarding the importance to organizations of converting receivables into cash.
Organizations usually employ many different strategies, technologies and methods in an attempt to reduce the average collection period for receivables. One approach is to optimize remittance collection by getting bills into the hands of the customers who are most willing or able to pay the bills. In some billing organizations, this approach is called customer-based work prioritization. Such prioritization is crucial for organizations with complex billing processes and large billing volumes. For such organizations, access to granular data that provides visibility into each discrete event, and the duration between these events, in the bill-to-payment lifecycle is typically critical. These events are not readily tracked by current methods and solutions for tracking a correspondence. Current solutions only provide a rough estimate of the actual time between when a customer receives a bill and when the customer remits payment of the bill. Therefore, a long-felt need exists for a method to determine much more precisely when the customer sends the remittance and to link the tracking information gathered for the bill to the tracking information gathered for the remittance.